Naval Architecture, Ocean and Civil Engineering

Application of an Improved GPU Acceleration Strategy for the Smoothed Particle Hydrodynamics Method

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  • 1. School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China
    2. College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China

Received date: 2022-06-13

  Revised date: 2022-08-09

  Accepted date: 2022-08-26

  Online published: 2022-11-10

Abstract

In order to solve the problem of graphics processing unit (GPU) memory access conflicts possibly caused by the disorder of particles and enhance the computation efficiency, an improved GPU acceleration strategy is proposed by establishing particle reorder technology. The acceleration strategy is applied to the smoothed particle hydrodynamics (SPH) method to simulate the dam breaking with obstacles in three dimensions, and the algorithm is verified by comparing with the experimental results, which obtained a high calculation accuracy. Based on this benchmark example of the SPH, the studies on the effect of particle renumbering and the solution efficiency of the algorithm are conducted by comparing the simulations of different hardware facilities. The results indicate that the particle reorder technology can ensure a stable single-step running time, and can effectively solve the problem of graphic card memory access conflicts that commonly exist in the GPU-SPH algorithm. Furthermore, the GPU parallel algorithm can greatly improve the solution efficiency of the SPH method, and with the increase of particle number, the advantage of drastically reducing the computation time becomes more obvious. The method proposed in this paper provides the possibility to expand the application of the SPH method to solve 3D numerical simulations.

Cite this article

GUAN Yanmin, YANG Caihong, KANG Zhuang, ZHOU Li . Application of an Improved GPU Acceleration Strategy for the Smoothed Particle Hydrodynamics Method[J]. Journal of Shanghai Jiaotong University, 2023 , 57(8) : 981 -987 . DOI: 10.16183/j.cnki.jsjtu.2022.209

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